Many drugs operate by chemically binding to specific molecular receptors. Molecular receptors typically are proteins (a term that includes glycoproteins and lipoproteins) in an animal such as a human being, and drug design and selection can be facilitated by accurately estimating the binding affinity of a drug to a protein, or, more generally, estimating the binding affinity of a ligand to a receptor, the term receptor being used to mean any moiety that specifically binds the ligand.
One way to determine receptor-ligand binding affinity uses the molecular structure that results when the ligand binds to the receptor (“the ligand-receptor complex”). Such structures may be studied by x-ray crystallography. The publicly accessible protein data bank (PDB) now contains more than 70,000 x-ray crystal structures, and pharmaceutical and biotechnology companies have an order of magnitude more proprietary structures. Many of these structures have been co-crystallized with small molecules bound to them. The examination of such structures, and deployment of the knowledge thereby gained to design new, more potent, and more specific inhibitors, is referred to as structure-based drug design.
Computational modeling facilitates structure-based drug design. One aspect of modeling detailed below involves scoring functions that use simulation techniques, such as molecular dynamics, Monte Carlo, or continuum electrostatics calculations. Scoring functions can be problematic when one is required to calculate a very small difference (the binding affinity) between two very large numbers (the free energies of the complex and of the separated protein and ligand). An alternative approach is to develop an empirical scoring function, based on the geometry of the complex, which directly evaluates the desired quantity. Such an approach has the advantage of being extremely fast as well as being amenable to fitting large amounts of which are now publicly available to experimental data. Commonly owned US 2007/0061118 A1 “Predictive scoring function for estimating binding affinity” (hereby incorporated by reference in its entirety) discloses such scoring functions.
It is desirable to increase the accuracy and robustness of scoring functions by making material improvements in the functional form that better reflect physical reality. Various patent applications that are commonly owned with this application describe ways to improve scoring functions: WO/2008/141260, entitled “Binding Affinity Scoring Function Including Factor For Environs Of Ring or Bulky Rigid Group”; U.S. Ser. No. 13/079,725, filed Apr. 4, 2011 entitled “Binding Affinity Scoring Function Penalizing Compounds which Make Unfavorable Hydrophobic Contacts With Quasilocalized Water Molecules in the Receptor Active Site”; and U.S. Ser. No. 13/079,489, filed Apr. 4, 2011 entitled “Scoring Function Penalizing Compounds which Desolvate Charged Protein Side Chains Structure”. Each of the above commonly owned applications is hereby incorporated by reference in its entirety.
A second major problem with scoring functions is that they may assign better (more negative) binding affinity scores to inactive compounds (i.e. compounds that would not be determined to bind to the receptor in a typical experimental screening protocol) than to active compounds. If a large number of inactive compounds are ranked ahead of active compounds, a principal function of docking—to discover new active compounds against a specified receptor from a very large compound library (often millions of compounds)—becomes difficult to carry out effectively. Therefore, substantially reducing the number of “false positives” (ranking of inactive compounds as active) would greatly improve scoring function performance.